Abstract
In this paper, we adopt a genetic-based machine learning (GBML) approach to realtime assignment problems, e.g., a container assignment problem, and propose a method of generating and selecting rules for assigning each container to a desirable position in a container yard. In applying the GBML, we use the Pitts approach, where the set of rules (rule-set) is represented symbolically as an individual of genetic algorithms, and the fitness of an individual is calculated based on the sum of time required for replacement to fetch each container in a prescribed order. We actually carried out some computational experiments, which indicate that the method of applying the GBML is effective for finding good rule-sets.
